Generalized Distribution Aggregation Protocol for Federated Statistical Heterogeneity.

Area of Science:

• Artificial Intelligence
• Machine Learning
• Distributed Systems

Background:

• Federated heterogeneity, encompassing data, model, and communication disparities, poses challenges in federated learning.
• Statistical heterogeneity often results in ineffective aggregation, leading to poor generalization and biased model weights.

Purpose of the Study:

• To address the performance degradation caused by federated heterogeneity.
• To develop a novel aggregation strategy that accounts for generalization bound disagreements.

Main Methods:

• Proposing a new weighting aggregation protocol based on distributional robustness analysis.
• Estimating upper and lower bounds of the second-order origin moment of shifted distributions for local models.
• Utilizing bound disagreements as aggregation proportions for model weights.

Main Results:

• The proposed aggregation protocol significantly enhances the performance of federated learning algorithms.
• Demonstrated improvements on several representative federated learning algorithms using benchmark datasets.
• The method effectively mitigates issues arising from statistical heterogeneity.

Conclusions:

• The novel weighting aggregation protocol offers a robust solution to federated heterogeneity.
• This approach improves generalization performance and stability of federated learning models.
• The findings provide a new direction for designing aggregation strategies in heterogeneous federated environments.